In the fields of computational modeling and high performance computing, modeling platforms are known which contain a modeling engine to receive a variety of modeling inputs, and then generate a precise modeled output based on those inputs. In conventional modeling platforms, the set of inputs are precisely known, or precisely controlled and the function applied to the modeling inputs is precisely known, but the ultimate results produced by the modeling engine are not known until the input data is supplied and the modeling engine is run. For example, in an econometric modeling platform, inputs for a particular industry like housing can be fed into a modeling engine. Those inputs can include, for instance, prevailing finance rates, employment rates, average new-home costs, costs of building materials, rate of inflation, and other economic or other variables that can be fed into the modeling engine which is programmed or configured to accept those inputs, apply a function or other processing to those inputs, and generate an output such as projected new-home sales for a given period of time. Those results can then be used to analyze or forecast other details related to the subject industry, such as predicted sector profits or employment.
In many real-life analytic applications, however, the necessary inputs for a given subject or interpolation run or analysis may not be known, while, at the same time, a desired or target output may be known or estimated with some accuracy. For instance, the budget for the research and development (R&D) department of a given corporation may be fixed at the beginning of a year or other budget cycle, but the assignment or allocation of that available amount of funds to different research teams or product areas may not be specified by managers or others. In such a case, an analyst may have to manually estimate and “back out” distributions of budget funds to different departments to begin to work out a set of component funding amounts that will, when combined, produce the already-known overall R&D or other budget. In performing that interpolation, the analyst may or may not be in possession of some departmental component budgets which have themselves also been fixed, or may or may not be in possession of the computation function which will appropriately sum or combine all component funds to produce the overall predetermined target budget. Adjustment of one component amount by hand may cause or suggest changes in other components in a ripple effect, which the analyst will then have to examine or account for in a further iteration of the same manual estimates.
In cases where an interpolation run is conducted, the ultimate selection of interpolated inputs and other data used to perform the interpolation may itself contain implied information regarding the appropriate breakdowns of the data, judgments about which inputs should receive priority compared to others, and other attributes of the eventual input breakouts and the interpolation function developed for that data. In cases, the values or user seeded data, both precise and non-precise for the interpolated inputs may be introduced by an analyst or other user acting to adjust those interpolated values, to determine different feasible alternative solutions.
In cases, it would aid the efficiency of the interpolation process if an initial set of interpolated input results could be generated directly from the raw set of operative data to be used in a new or updated interpolation feasibility analysis. Thus, for instance, a user performing a analysis on a set of medical data related to epidemiology may wish to interpolate desirable inoculation rates, doses, age cohorts, and/or other data related to an infectious season for the influenza virus for the current year. To begin that analysis, the user would, without the aid of other tools or services, have to apply an interpolation approach, including processing one, some, or all of potentially very large-scale medical data, to arrive at a first set of interpolated results to consider for their analysis or report. In cases where the operative data consists of very large-scale data, such as terabytes, petabytes, exabytes, and/or other amounts of data, that initial processing could represent a significant load on servers or other processor resources, requiring possibly hours or days of delay to complete. It may be desirable to provide systems and methods for generating an interpolation data template to normalize analytic runs, in which a set of selectable interpolation templates can be accessed to discover one or more feasible templates that match or partially match the content, nature, and/or attributes of the data under analysis, to permit an initial set of results to be produced directly from the template, representing normalized or estimated interpolation outcomes derived from studies of similar data sets in the past.